Millions of people undergo traumatic experiences annually. While acute distress is a normative response to trauma, a small percent of the people who have undergone traumatic experiences continue to exhibit severe stress reactions long after the trauma. The posttraumatic reactions may include intrusive memories, hypervig- ilant arousal, impaired concentration, depression, emotional detachment from others, and disengagement from aspects of life that provide meaning and self-fulfillment. In functional assessments these recurrent reactions seriously impair intrapersonal, interpersonal, and occupational functioning. Despite these ramifications, post-traumatic stress disorders are under-reported, and in many cases go completely undetected. Barriers to help-seeking include: lack of knowledge about predisposition symptoms, the unavailability of appropriate remedial/prevention services, the fear of the social stigma of mental illness, lack of social support, or assumptions that the mood changes are a part of the overwhelming nature of post-crisis life. In recent times, many crisis rehabilitation efforts have recognized these challenges, and have incorporated access to mental health services in their intervention and support programs. However, treatment efforts, that typically comprise medication, therapy, or both, are more successful with early intervention; in fact, the likelihood of achieving full recovery decline as the illness lengthens. Social media such as Twitter and Facebook are increasingly serving an important role in crisis situations: aggregating and disseminating information, while providing opportunities for reflection and discussion of collective grief and trauma. These platforms thus provide an opportunity to investigate if post-traumatic stress and anxiety can be detected on a macro scale by studying the affective responses of crisis-inflicted populations. Due to social media's archival record, reactions to societal crisis and traumatic happenings can be tracked longitudinally. We propose to mine behavioral data from these platforms to better understand the range of responses of people to crises. By applying state-of-the-art techniques from text and social network analytics, we will go be- yond population-level estimates of crisis behavior, to gauge the social, psychological, emotional, and linguistic attributes of specific crisis-laden communities, and the relationship of these behavioral attributes to key mental and public health outcomes in a crisis context. Specifically, we will accomplish the following research aims: 1. Design data mining techniques that can intelligently filter social media posts for crisis-relevant content, incorporating statistical correction methods that can extract population representative samples. 2. Use linguistic signals and social network metadata to identify key communities in the crisis-laden population. 3. Develop behavioral measures from the activities of these communities that reflect the extent of their risk to stress disorders from the crisis situation. Ths will include measures that automatically expand and refine existing technologies to match community-specific language and dialects, which can vary dramatically in social media writing, as well as factor in the unique context of crisis events. 4. Perform a longitudinal study of the behavior of crisis-embroiled communities, identifying the factors that indicate especially high-ris groups. If successful, this research will (a) bring to the fore variables related to the exacerbaton of (or even predisposition to) PTSD, (b) enable new mechanisms to identify at-risk communities in a near real-time fashion, and (c) lend a complementary perspective on current research around trauma diagnosis, which relies typically on laboratory studies and surveys. On a practical note, a potential link between behavior manifested in social media in the context of a crisis, and anxiety and post-traumatic stress symptomatology can also augment traditional efforts in providing valuable interventions as a part of disaster response. This work is a close collaboration with computational linguist and faculty of School of Interactive Computing at Georgia Institute of Technology, Dr. Jacob Eisenstein, who is an expert in the discovery of social relationships latent in linguistic data, and Dr. James Pennebaker, a social psychologist and faculty in the Department of Psychology at the University of Texas, Austin, whose expertise is in the area of psychological interpretation of language cues.